Abstract
Smart systems, ranging from smart homes to infrastructure networks such as traffic and power networks, are examples of cyber-physical systems that are oftentimes safety critical, yet prone to system failures. This chapter contributes to the area of passive fault detection and isolation for such systems, modeled as hybrid dynamical systems, from a model invalidation perspective. In particular, we present a model-based approach for guaranteed detection and isolation of generic faults in cyber-physical systems, where both the systems and the faults are represented by hidden-mode switched affine models with time-varying parametric uncertainty subject to process and measurement noise. We show that model invalidation based fault detection and isolation can be reduced to the feasibility of a mixed-integer linear programming (MILP) problem, which can be solved efficiently by leveraging state-of-the-art MILP solvers. In addition, for a given pair of models (system and/or fault models), we introduce the notion of T-distinguishability and show that the T-distinguishability test for any pair of models also reduces to a feasibility check of a MILP problem. Using this property, we show that the satisfaction of the T-distinguishability property with a finite T allows us to implement the model invalidation algorithm using only data from a finite horizon with guarantees of fault detection and isolation in a receding horizon manner. Finally, building on these results, a real-time fault detection and isolation scheme is presented, which runs multiple model invalidation problems simultaneously at run-time with guarantees for the detection and isolation delays when identifying specific faults.
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Notes
- 1.
For convenience, we will use the term “fault” to refer to any fault, attack or anomaly throughout this chapter. Note that our proposed approach is primarily concerned with the detection and isolation of changes in dynamical system behavior and is indifferent to the nature of the observed changes, i.e., whether they are accidental faults or strategic attacks, either cyber or physical.
- 2.
- 3.
These faults can also be consequences of cyber or physical attacks. For instance, the bias in the humidity sensor can be a result of a false data injection attack (a common form of cyberattack).
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Acknowledgements
This work is supported in part by DARPA grant N66001-14-1-4045 and an Early Career Faculty grant from NASA’s Space Technology Research Grants Program.
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Harirchi, F., Yong, S.Z., Ozay, N. (2018). Passive Diagnosis of Hidden-Mode Switched Affine Models with Detection Guarantees via Model Invalidation. In: Sayed-Mouchaweh, M. (eds) Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-74962-4_9
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DOI: https://doi.org/10.1007/978-3-319-74962-4_9
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